1
Combinatorial optimization has found applications in numerous fields, from aerospace to transportation planning and economics. The goal …
Coronavirus Disease 2019 (COVID-19) has spread aggressively across the world causing an existential health crisis. Thus, having a …
Data augmentation is a key practice in machine learning for improving generalization performance. However, finding the best data …
Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs). However, as the …
Adversarial Imitation Learning alternates between learning a discriminator – which tells apart expert’s demonstrations from …
In suitably initialized wide networks, small learning rates transform deep neural networks (DNNs) into neural tangent kernel (NTK) …
Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the …
One of the principal scientific challenges in deep learning is explaining generalization, i.e., why the particular way the community …
We are interested in understanding how well Transformer language models (TLMs) can perform reasoning tasks when trained on knowledge …
Continual learning studies agents that learn from streams of tasks without forgetting previous ones while adapting to new ones. Two …